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At least 451 records · Page 25

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step chemical named entity recognition model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that the “chemical similarity” of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.

36 MATERIALS SCIENCE↗

Interferometric measurements of refractive index and dispersion at high pressure

Abstract We describe a high precision interferometer system to measure the pressure dependence of the refractive index and its dispersion in the diamond anvil cell (DAC). The reflective Fabry–Perot fringe patterns created by both a white light and a monochromatic beam are recorded to determine both the sample thickness and its index at the laser wavelength and to characterize the dispersion in the visible range. Advances in sample preparation, optical setup, and data analysis enable us to achieve $$10^{-4}$$ 10 - 4 random uncertainty, demonstrated with an air sample, a factor of five improvement over the best previous DAC measurement. New data on $${\text{H}}_{2}\text{O}$$ H 2 O liquid water and ice VI up to 2.21 GPa at room temperature illustrate how higher precision measurements of the index and its optical dispersion open up new opportunities to reveal subtle changes in the electronic structure of water at high pressure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗

Evidence for pseudo–Jahn-Teller distortions in the charge density wave phase of 1$\textit{T}-\text{TiSe}_2$

Here, the charge density wave (CDW) instability in 1$\textit{T}-\text{TiSe}_2$ is revisited to investigate its electronic and structural origins using angle resolved photoemission spectroscopy (ARPES), neutron diffraction, and density functional theory (DFT) calculations. The evidence for Jahn-Teller (JT)-like distortions in the CDW state is provided from the local structure analysis that shows splitting of the Ti-Se bonds into short and long. The magnitude of the split is inconsistent with the commonly accepted Di Salvo model for the structural distortions in the CDW phase. From the APRES data, it is deduced that a nontrivial temperature-dependent energy shift of the Se $4\textit{p}$ valence bands occurs, but is absent for the Ti $3\textit{d}$ conduction bands. Collectively, these observations suggest that a JT-like mechanism is most likely central to the CDW instability in 1$\textit{T}-\text{TiSe}_2$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations

This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps

99 GENERAL AND MISCELLANEOUS↗

Dark matter substructure or source model systematics? A case study of cluster lens Abell S1063

Mapping the small-scale structure of the universe through gravitational lensing is a promising tool for probing the particle nature of dark matter. Curved Arc Basis (CAB) has been proposed as a local lensing formalism in galaxy clusters, with the potential to detect low-mass dark matter substructure. In this work, we analyse the cluster lens Abell S1063 in search of dark matter substructure with the CAB formalism, using multiband imaging data from James Webb Space Telescope ( JWST ). We use two different source modelling methods: shapelets and pixel-based source reconstruction based on Delaunay triangulation. We find that source modelling systematics from shapelets result in a disagreement between CAB parameters measured from different filters. Source modelling with Delaunay significantly alleviates this systematic, as seen in the improvement in agreement across filters. We also find that inadequate complexity in source modelling can result in convincing spurious detections of dark matter substructure from strong gravitational lenses, as seen by our $\Delta \text{BIC} > 20$ measurement of a $M \sim 10^{10}$ ${\rm M}_{\odot }$ subhalo with shapelets, a spurious detection that is not reproduced with Delaunay source modelling. We demonstrate that multiband analysis with different JWST filters is key for disentangling source and lens model systematics from dark matter substructure detections.

79 ASTRONOMY AND ASTROPHYSICS↗

Transient Data Library of Solar Grid Integrated Distributed System

This submission contains an open-source library of transient events in distributed system with high solar PV. The library includes the collected data, related documents and scripts for loading the data. The data library is built for transient event detection and machine learning based analysis algorithm development. The data was collected via both field test and software simulation. The units for the data are included in the data file headers for each data series. A text editor or spreadsheet software, such as Excel, and Matlab is required to view the data.

algorithms↗

Radiation Safety System

The goal of this work is to provide an overview of a Radiation safety system (RSS) designed for protection from prompt radiation hazard at accelerator facilities. RSS design parameters, functional requirements and constraints are derived from hazard analysis and risk assessment undertaken in the design phase of the facility. The two main subsystems of a RSS are access control system (ACS) and radiation control system (RCS). In this text, a common approach to risk assessment, typical components of ACS and RCS, desirable features and general design principles applied to RSS are described.

43 PARTICLE ACCELERATORS↗

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗

Survey of Current State of the Art Entity-Relation Extraction Tools

In the area of information extraction from text data, there exists a number of tools with the capability of extracting entities, topics, and their relationships with one another from both structured and unstructured text sources. Such information has endless uses in a number of domains, however, the solutions to getting this information are still in early stages and has room for improvement. The topic has been explored from a research perspective by academic institutions, as well as formal tool creation from corporations but has not made much advancement since the early 2000's. Overall, entity extraction, and the related topic of entity linking, is common among these tools, though with varying degrees of accuracy, while relationship extraction is more difficult to find and seems limited to same sentence analysis. In this report, we take a look at the top state of the art tools currently available and identify their capabilities, strengths, and weaknesses. We explore the common algorithms in the successful approaches to entity extraction and their ability to efficiently handle both structured and unstructured text data. Finally, we highlight some of the common issues among these tools and summarize the current ability to extract relationship information.

97 MATHEMATICS AND COMPUTING↗

IR Thermographic Analysis of 3D Printed CFRP Reference Samples with Back-Drilled and Embedded Defects

Carbon-fiber composite structures may demonstrate a defective behavior due to manufacturing induced anomalies (delamination, dis-bonds) or service related defectives (impact damage, water ingress). Thus, there is a need for a relatively fast and low cost non-intrusive testing schemes such as infrared thermography (IRT). Still, thermography testing requires calibrated samples and coupons to yield best results. The presented research demonstrates the novel use of 3D printing technology to generate IRT calibration samples. In this text, two carbon fiber reinforced polymer samples are 3D printed; the first mimics a 'back-drilled holes' type coupons, while the other is designed to embed air pockets similar to Teflon inserts. The generated samples are then tested using two IRT modalities; namely pulse thermography and lock-in thermography. Furthermore, the resulted thermograms are processed using a principle component analysis, to help highlight the variance of defectives in a consistent manner among the samples. This research findings offer insights on the variation of detectability between embedded and back-printed samples, which might be due to the inserts thickness.

36 MATERIALS SCIENCE↗

Meteorological data from the Billy Bar site at the East River, Colorado

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

54 ENVIRONMENTAL SCIENCES↗

ATLAS, an integrated structural analysis and design system. Volume 3: User's manual, input and execution data

The input data and execution control statements for the ATLAS integrated structural analysis and design system are described. It is operational on the Control Data Corporation (CDC) 6600/CYBER computers in a batch mode or in a time-shared mode via interactive graphic or text terminals. ATLAS is a modular system of computer codes with common executive and data base management components. The system provides an extensive set of general-purpose technical programs with analytical capabilities including stiffness, stress, loads, mass, substructuring, strength design, unsteady aerodynamics, vibration, and flutter analyses. The sequence and mode of execution of selected program modules are controlled via a common user-oriented language.

Dreisbach, R. L.↗

The Cool Flames Experiment

A space-based experiment is currently under development to study diffusion-controlled, gas-phase, low temperature oxidation reactions, cool flames and auto-ignition in an unstirred, static reactor. At Earth's gravity (1g), natural convection due to self-heating during the course of slow reaction dominates diffusive transport and produces spatio-temporal variations in the thermal and thus species concentration profiles via the Arrhenius temperature dependence of the reaction rates. Natural convection is important in all terrestrial cool flame and auto-ignition studies, except for select low pressure, highly dilute (small temperature excess) studies in small vessels (i.e., small Rayleigh number). On Earth, natural convection occurs when the Rayleigh number (Ra) exceeds a critical value of approximately 600. Typical values of the Ra, associated with cool flames and auto-ignitions, range from 104-105 (or larger), a regime where both natural convection and conduction heat transport are important. When natural convection occurs, it alters the temperature, hydrodynamic, and species concentration fields, thus generating a multi-dimensional field that is extremely difficult, if not impossible, to be modeled analytically. This point has been emphasized recently by Kagan and co-workers who have shown that explosion limits can shift depending on the characteristic length scale associated with the natural convection. Moreover, natural convection in unstirred reactors is never "sufficiently strong to generate a spatially uniform temperature distribution throughout the reacting gas." Thus, an unstirred, nonisothermal reaction on Earth does not reduce to that generated in a mechanically, well-stirred system. Interestingly, however, thermal ignition theories and thermokinetic models neglect natural convection and assume a heat transfer correlation of the form: q=h(S/V)(T(bar) - Tw) where q is the heat loss per unit volume, h is the heat transfer coefficient, S/V is the surface to volume ratio, and (T(bar) - Tw ) is the spatially averaged temperature excess. This Newtonian form has been validated in spatially-uniform, well-stirred reactors, provided the effective heat transfer coefficient associated with the unsteady process is properly evaluated. Unfortunately, it is not a valid assumption for spatially-nonuniform temperature distributions induced by natural convection in unstirred reactors. "This is why the analysis of such a system is so difficult." Historically, the complexities associated with natural convection were perhaps recognized as early as 1938 when thermal ignition theory was first developed. In the 1955 text "Diffusion and Heat Exchange in Chemical Kinetics", Frank-Kamenetskii recognized that "the purely conductive theory can be applied at sufficiently low pressure and small dimensions of the vessel when the influence of natural convection can be disregarded." This was reiterated by Tyler in 1966 and further emphasized by Barnard and Harwood in 1974. Specifically, they state: "It is generally assumed that heat losses are purely conductive. While this may be valid for certain low pressure slow combustion regimes, it is unlikely to be true for the cool flame and ignition regimes." While this statement is true for terrestrial experiments, the purely conductive heat transport assumption is valid at microgravity (mu-g). Specifically, buoyant complexities are suppressed at mu-g and the reaction-diffusion structure associated with low temperature oxidation reactions, cool flames and auto-ignitions can be studied. Without natural convection, the system is simpler, does not require determination of the effective heat transfer coefficient, and is a testbed for analytic and numerical models that assume pure diffusive transport. In addition, mu-g experiments will provide baseline data that will improve our understanding of the effects of natural convection on Earth.

Pearlman, Howard↗

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗

Search for heavy Majorana neutrinos in pp collisions at √s = 8 TeV with the CMS detector & photodetector and calorimeter R&D for particle colliders

This thesis contains both physics analysis and hardware studies. It consists of two primary sections: the results of a search for heavy Majorana mass neutrinos, using the event signature of same (like) sign charged electron pairs ($e^{\pm} e^{\pm}$ ) and two jets, and the results of studies to upgrade the Hadronic Forward (HF) and Hadronic Endcap (HE) subdetectors in the Compact Muon Solenoid (CMS) detector in response to the high intensity proton-proton collisions generated at the Large Hadron Collider (LHC) at European Organization for Nuclear Research (CERN, Conseil Europ\'{e}en pour la Recherche Nucl\'{e}aire). In this search for Majorana mass neutrinos, same sign dielectron ($e^{\pm} e^{\pm}$) + dijet events in the final state have been considered as a signature for neutrino particles. The analyzed data corresponds to an integrated luminosity of 19.7 fb\textsuperscript{-1} of proton-proton collisions at a center of mass energy of $\sqrt{s} = 8 \text{TeV}$, collected using the CMS detector during the 2012 operation at the LHC. Monte Carlo simulations accounting for the theoretical expectations of the Standard Model (SM) and the detector limitations are used to prototype the experiment and to test proposed analysis steps. No excess of events is observed in the data beyond the expected SM background. Upper limits are set on the mixing element squared, $|{V}_{eN}|^{2}$, of the heavy Majorana neutrino with standard model neutrinos, as a function of Majorana neutrino mass for masses in the range of 40-500 $GeV/c^2$. The detector upgrade search comprises three sections of this thesis. The first section describes the test results of 1785 multianode Hamamatsu R7600U-200-M4 photomultiplier tubes (PMT) in numerous parameters such as gain, dark current, and timing characteristics, which provide insights on the expected performance of the upgraded CMS-HF detector. These PMTs replaced the previous single anode R7525 PMTs because the glass windows of previous PMTs are the source of Cherenkov radiation, which causes a background noise in the experiment. The second section reports characterization results of two types of PMTs in a novel operation mode for Secondary Emission (SE) Ionization Calorimetry, which is a novel technique to measure electromagnetic shower particles in extreme radiation environments. The third section presents the test results of novel scintillating materials for CMS experiment in specific and future particle accelerators in general. These materials are Polyethylene Naphthalate (PEN), Polyethylene Terephthalate (PET), high efficiency mirror (HEM) and quartz plates with various organic and inorganic coating materials such as p-Terphenyl (pTp), Anthracene and Gallium-doped Zinc Oxide (ZnO:Ga). We have investigated them for radiation hardness, light yield, timing characteristics, and scintillation and transmission properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Statistical properties of DNA sequences

We review evidence supporting the idea that the DNA sequence in genes containing non-coding regions is correlated, and that the correlation is remarkably long range--indeed, nucleotides thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the "non-stationarity" feature of the sequence of base pairs by applying a new algorithm called detrended fluctuation analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and non-coding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to every DNA sequence (33301 coding and 29453 non-coding) in the entire GenBank database. Finally, we describe briefly some recent work showing that the non-coding sequences have certain statistical features in common with natural and artificial languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts. These statistical properties of non-coding sequences support the possibility that non-coding regions of DNA may carry biological information.

Non-NASA Center↗

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗